# Adapted from https://github.com/fregu856/deeplabv3

import torch
import torch.nn as nn
import torch.nn.functional as F


class ASPP(nn.Module):
    def __init__(self, num_classes):
        super(ASPP, self).__init__()

        self.conv_1x1_1 = nn.Conv2d(512, 256, kernel_size=1)
        self.bn_conv_1x1_1 = nn.BatchNorm2d(256)

        self.conv_3x3_1 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=6, dilation=6)
        self.bn_conv_3x3_1 = nn.BatchNorm2d(256)

        self.conv_3x3_2 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=12, dilation=12)
        self.bn_conv_3x3_2 = nn.BatchNorm2d(256)

        self.conv_3x3_3 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=18, dilation=18)
        self.bn_conv_3x3_3 = nn.BatchNorm2d(256)

        self.avg_pool = nn.AdaptiveAvgPool2d(1)

        self.conv_1x1_2 = nn.Conv2d(512, 256, kernel_size=1)
        self.bn_conv_1x1_2 = nn.BatchNorm2d(256)

        self.conv_1x1_3 = nn.Conv2d(1280, 256, kernel_size=1) # (1280 = 5*256)
        self.bn_conv_1x1_3 = nn.BatchNorm2d(256)

        self.conv_1x1_4 = nn.Conv2d(256, num_classes, kernel_size=1)

    def forward(self, feature_map):
        # (feature_map has shape (batch_size, 512, h/16, w/16)) (assuming self.resnet is ResNet18_OS16 or ResNet34_OS16. If self.resnet instead is ResNet18_OS8 or ResNet34_OS8, it will be (batch_size, 512, h/8, w/8))

        feature_map_h = feature_map.size()[2] # (== h/16)
        feature_map_w = feature_map.size()[3] # (== w/16)

        out_1x1 = F.relu(self.bn_conv_1x1_1(self.conv_1x1_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
        out_3x3_1 = F.relu(self.bn_conv_3x3_1(self.conv_3x3_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
        out_3x3_2 = F.relu(self.bn_conv_3x3_2(self.conv_3x3_2(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
        out_3x3_3 = F.relu(self.bn_conv_3x3_3(self.conv_3x3_3(feature_map))) # (shape: (batch_size, 256, h/16, w/16))

        out_img = self.avg_pool(feature_map) # (shape: (batch_size, 512, 1, 1))
        out_img = F.relu(self.bn_conv_1x1_2(self.conv_1x1_2(out_img))) # (shape: (batch_size, 256, 1, 1))
        out_img = torch.nn.functional.interpolate(out_img, size=(feature_map_h, feature_map_w), mode="bilinear", align_corners=True) # (shape: (batch_size, 256, h/16, w/16))

        out = torch.cat([out_1x1, out_3x3_1, out_3x3_2, out_3x3_3, out_img], 1) # (shape: (batch_size, 1280, h/16, w/16))
        out = F.relu(self.bn_conv_1x1_3(self.conv_1x1_3(out))) # (shape: (batch_size, 256, h/16, w/16))
        out = self.conv_1x1_4(out) # (shape: (batch_size, num_classes, h/16, w/16))

        return out


class ASPP_Bottleneck(nn.Module):
    def __init__(self, num_classes):
        super(ASPP_Bottleneck, self).__init__()

        self.conv_1x1_1 = nn.Conv2d(4*512, 256, kernel_size=1)
        self.bn_conv_1x1_1 = nn.BatchNorm2d(256)

        self.conv_3x3_1 = nn.Conv2d(4*512, 256, kernel_size=3, stride=1, padding=6, dilation=6)
        self.bn_conv_3x3_1 = nn.BatchNorm2d(256)

        self.conv_3x3_2 = nn.Conv2d(4*512, 256, kernel_size=3, stride=1, padding=12, dilation=12)
        self.bn_conv_3x3_2 = nn.BatchNorm2d(256)

        self.conv_3x3_3 = nn.Conv2d(4*512, 256, kernel_size=3, stride=1, padding=18, dilation=18)
        self.bn_conv_3x3_3 = nn.BatchNorm2d(256)

        self.avg_pool = nn.AdaptiveAvgPool2d(1)

        self.conv_1x1_2 = nn.Conv2d(4*512, 256, kernel_size=1)
        self.bn_conv_1x1_2 = nn.BatchNorm2d(256)

        self.conv_1x1_3 = nn.Conv2d(1280, 256, kernel_size=1) # (1280 = 5*256)
        self.bn_conv_1x1_3 = nn.BatchNorm2d(256)

        self.conv_1x1_4 = nn.Conv2d(256, num_classes, kernel_size=1)

    def forward(self, feature_map):
        # (feature_map has shape (batch_size, 4*512, h/16, w/16))

        feature_map_h = feature_map.size()[2] # (== h/16)
        feature_map_w = feature_map.size()[3] # (== w/16)

        out_1x1 = F.relu(self.bn_conv_1x1_1(self.conv_1x1_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
        out_3x3_1 = F.relu(self.bn_conv_3x3_1(self.conv_3x3_1(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
        out_3x3_2 = F.relu(self.bn_conv_3x3_2(self.conv_3x3_2(feature_map))) # (shape: (batch_size, 256, h/16, w/16))
        out_3x3_3 = F.relu(self.bn_conv_3x3_3(self.conv_3x3_3(feature_map))) # (shape: (batch_size, 256, h/16, w/16))

        out_img = self.avg_pool(feature_map) # (shape: (batch_size, 512, 1, 1))
        out_img = F.relu(self.bn_conv_1x1_2(self.conv_1x1_2(out_img))) # (shape: (batch_size, 256, 1, 1))
        out_img = torch.nn.functional.interpolate(out_img, size=(feature_map_h, feature_map_w), mode="bilinear", align_corners=True) # (shape: (batch_size, 256, h/16, w/16))

        out = torch.cat([out_1x1, out_3x3_1, out_3x3_2, out_3x3_3, out_img], 1) # (shape: (batch_size, 1280, h/16, w/16))
        out = F.relu(self.bn_conv_1x1_3(self.conv_1x1_3(out))) # (shape: (batch_size, 256, h/16, w/16))
        out = self.conv_1x1_4(out) # (shape: (batch_size, num_classes, h/16, w/16))

        return out
